Related papers: Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

URL: http://arxiv.org/abs/2307.15377v1
Date: Fri, 28 Jul 2023 07:53:34 GMT
Title: Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
Authors: Junhyun Lee, Bumsoo Kim, Minji Jeon, Jaewoo Kang
Abstract summary: Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. We propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks.
Score: 19.58671020943416
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for graph-structured data (e.g., scene graph matching, code searching, and drug-drug interaction prediction). To this end, recent works have shifted their focus to learning the interaction between pairs of graphs. Despite their improved performance, these works were still limited in that the interactions were considered at the node-level, resulting in high computational costs and suboptimal performance. To address this issue, we propose a novel and efficient graph-level approach for extracting interaction representations using co-attention in graph pooling. Our method, Co-Attention Graph Pooling (CAGPool), exhibits competitive performance relative to existing methods in both classification and regression tasks using real-world datasets, while maintaining lower computational complexity.

Related papers

Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
arXiv Detail & Related papers (2023-03-27T17:39:00Z)
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning. We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning. We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z)
Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
arXiv Detail & Related papers (2021-09-24T08:01:23Z)
Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
arXiv Detail & Related papers (2021-09-01T08:24:02Z)
Graph Pooling via Coarsened Graph Infomax [9.045707667111873]
We propose Coarsened GraphPool Infomaxing (CGI) to maximize the mutual information between the input and the coarsened graph of each pooling layer. To achieve mutual information neural, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples.
arXiv Detail & Related papers (2021-05-04T03:50:21Z)
Multilevel Graph Matching Networks for Deep Graph Similarity Learning [79.3213351477689]
We propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects. To compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks.
arXiv Detail & Related papers (2020-07-08T19:48:19Z)
Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
arXiv Detail & Related papers (2020-06-19T13:09:44Z)
CoSimGNN: Towards Large-scale Graph Similarity Computation [5.17905821006887]
Graph Neural Networks (GNNs) provide a data-driven solution for this task. Existing GNN-based methods, which either respectively embeds two graphs or deploy cross-graph interactions for whole graph pairs, are still not able to achieve competitive results. We propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.
arXiv Detail & Related papers (2020-05-14T16:33:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.